Agent delegation

Build multi-agent systems where specialized agents work together

Agent delegation is a powerful feature in Tyler that allows you to create specialized agents that can work together to solve complex problems. This pattern enables you to build sophisticated AI systems where each agent has a specific role and expertise.

💻 Code Examples

Agent Delegation

Multi-agent coordination patterns

A2A Multi-Agent

Cross-platform agent networks

Overview

Agent delegation allows one agent (the coordinator) to delegate tasks to other specialized agents. This is useful when:

• Different parts of a task require different expertise
• You want to separate concerns and create modular systems
• You need different models or configurations for different subtasks
• You want to build scalable, maintainable AI systems

Basic Delegation

Here’s how to create a simple multi-agent system:

from tyler import Agent
from lye import WEB_TOOLS, FILES_TOOLS, IMAGE_TOOLS

# Create specialized agents
research_agent = Agent(
    name="Researcher",
    model_name="gpt-4",
    purpose="To conduct thorough research on any topic",
    tools=WEB_TOOLS
)

writer_agent = Agent(
    name="Writer",
    model_name="gpt-4",
    purpose="To write clear, engaging content based on research",
    tools=FILES_TOOLS
)

# Create coordinator that can delegate
coordinator = Agent(
    name="Project Manager",
    model_name="gpt-4",
    purpose="To coordinate research and writing tasks",
    agents=[research_agent, writer_agent]
)

# The coordinator automatically delegates based on the task
thread = Thread()
thread.add_message(Message(
    role="user",
    content="Research the latest AI trends and write a report"
))

result = await coordinator.run(thread)

How Delegation Works

When an agent has access to other agents:

1. Automatic Detection: The coordinator analyzes the task to determine if delegation would be helpful
2. Agent Selection: It chooses the most appropriate agent based on their purpose and capabilities
3. Task Delegation: The coordinator formulates a clear request for the specialized agent
4. Result Integration: The coordinator receives and integrates the results into its response

Advanced patterns

Hierarchical teams

Create multi-level agent hierarchies:

# Data team
data_analyst = Agent(
    name="Data Analyst",
    model_name="gpt-4",
    purpose="To analyze data and create visualizations",
    tools=[*IMAGE_TOOLS, "python_repl"]
)

data_engineer = Agent(
    name="Data Engineer",
    model_name="gpt-3.5-turbo",
    purpose="To handle data pipelines and ETL processes",
    tools=FILES_TOOLS
)

data_lead = Agent(
    name="Data Team Lead",
    model_name="gpt-4",
    purpose="To coordinate data projects",
    agents=[data_analyst, data_engineer]
)

# Engineering team
backend_dev = Agent(
    name="Backend Developer",
    model_name="gpt-4",
    purpose="To develop backend services and APIs",
    tools=["python_repl", *FILES_TOOLS]
)

frontend_dev = Agent(
    name="Frontend Developer",
    model_name="gpt-4",
    purpose="To build user interfaces",
    tools=FILES_TOOLS
)

tech_lead = Agent(
    name="Tech Lead",
    model_name="gpt-4",
    purpose="To coordinate development tasks",
    agents=[backend_dev, frontend_dev]
)

# Project coordinator with access to team leads
project_manager = Agent(
    name="Project Manager",
    model_name="gpt-4",
    purpose="To manage complex projects requiring multiple teams",
    agents=[data_lead, tech_lead]
)

Specialized agent networks

Create agents that can collaborate peer-to-peer:

# Create a network of specialized agents
agents = {
    "researcher": Agent(
        name="Researcher",
        model_name="gpt-4",
        purpose="To find and synthesize information",
        tools=WEB_TOOLS
    ),
    "fact_checker": Agent(
        name="Fact Checker",
        model_name="gpt-4",
        purpose="To verify claims and check sources",
        tools=WEB_TOOLS
    ),
    "editor": Agent(
        name="Editor",
        model_name="gpt-4",
        purpose="To improve clarity and correctness",
        tools=[]
    )
}

# Give each agent access to others for collaboration
for agent_name, agent in agents.items():
    agent.agents = [a for n, a in agents.items() if n != agent_name]

# Use any agent as entry point
thread = Thread()
thread.add_message(Message(
    role="user",
    content="Write a fact-checked article about quantum computing"
))

# The researcher might delegate to fact_checker and editor
result = await agents["researcher"].run(thread)

Dynamic agent selection

Choose agents based on task requirements:

class DynamicCoordinator:
    def __init__(self):
        self.agent_pool = {
            "creative": Agent(
                name="Creative",
                model_name="gpt-4",
                temperature=0.9,
                purpose="For creative and imaginative tasks"
            ),
            "analytical": Agent(
                name="Analytical",
                model_name="gpt-4",
                temperature=0.1,
                purpose="For precise, logical analysis"
            ),
            "coder": Agent(
                name="Coder",
                model_name="gpt-4",
                purpose="For programming tasks",
                tools=["python_repl", *FILES_TOOLS]
            )
        }
    
    async def handle_task(self, task: str) -> Any:
        # Analyze task to select agents
        selected_agents = []
        
        task_lower = task.lower()
        if any(word in task_lower for word in ["create", "imagine", "design"]):
            selected_agents.append(self.agent_pool["creative"])
        
        if any(word in task_lower for word in ["analyze", "compare", "evaluate"]):
            selected_agents.append(self.agent_pool["analytical"])
        
        if any(word in task_lower for word in ["code", "program", "implement"]):
            selected_agents.append(self.agent_pool["coder"])
        
        # Create coordinator with selected agents
        coordinator = Agent(
            name="Dynamic Coordinator",
            model_name="gpt-4",
            purpose=f"To coordinate: {task}",
            agents=selected_agents
        )
        
        thread = Thread()
        thread.add_message(Message(role="user", content=task))
        return await coordinator.run(thread)

Best practices

1. Clear Purpose Definition

Each agent should have a well-defined purpose:

# Good - specific and clear
agent = Agent(
    name="Python Expert",
    purpose="To write, review, and debug Python code following PEP 8 standards"
)

# Too vague
agent = Agent(
    name="Helper",
    purpose="To help with stuff"
)

2. Appropriate Tool Assignment

Give agents only the tools they need:

# Security researcher doesn't need file write access
security_agent = Agent(
    name="Security Auditor",
    purpose="To analyze code for security vulnerabilities",
    tools=["read_file", "search"]  # Read-only tools
)

# Code fixer needs write access
fix_agent = Agent(
    name="Security Fixer",
    purpose="To fix identified security issues",
    tools=FILES_TOOLS  # Full file access
)

3. Model Selection

Choose appropriate models for each agent:

# Use more powerful model for complex reasoning
architect = Agent(
    name="System Architect",
    model_name="gpt-4",
    purpose="To design complex system architectures"
)

# Use faster/cheaper model for simple tasks
formatter = Agent(
    name="Code Formatter",
    model_name="gpt-3.5-turbo",
    purpose="To format code according to style guides"
)

4. Delegation Depth

Limit delegation depth to avoid complexity:

# Configure maximum delegation depth
coordinator = Agent(
    name="Coordinator",
    agents=[agent1, agent2],
    metadata={"max_delegation_depth": 2}
)

Common Patterns

Research and Analysis Pipeline

# Sequential pipeline of agents
pipeline = [
    Agent(name="Gatherer", purpose="To collect raw information", tools=WEB_TOOLS),
    Agent(name="Analyzer", purpose="To analyze and extract insights"),
    Agent(name="Reporter", purpose="To create final report", tools=FILES_TOOLS)
]

async def run_pipeline(task: str):
    context = task
    for agent in pipeline:
        thread = Thread()
        thread.add_message(Message(role="user", content=context))
        result = await agent.run(thread)
        context = result.new_messages[-1].content
    return context

Consensus building

Multiple agents provide input:

# Get perspectives from different agents
perspectives = []
for agent in [optimist_agent, pessimist_agent, realist_agent]:
    thread = Thread()
    thread.add_message(Message(
        role="user",
        content="What are the implications of AGI?"
    ))
    result = await agent.run(thread)
    perspectives.append({
        "agent": agent.name,
        "view": result.new_messages[-1].content
    })

# Synthesizer agent combines perspectives
synthesizer = Agent(
    name="Synthesizer",
    purpose="To combine multiple viewpoints into balanced analysis"
)

thread = Thread()
thread.add_message(Message(
    role="user",
    content=f"Synthesize these perspectives on AGI: {perspectives}"
))
result = await synthesizer.run(thread)

Performance Considerations

1. Parallel Delegation

When agents work independently, run them in parallel:

import asyncio

async def parallel_research(topics: List[str]):
    research_tasks = []
    
    for topic in topics:
        agent = Agent(
            name=f"Researcher-{topic}",
            purpose=f"To research {topic}",
            tools=WEB_TOOLS
        )
        
        thread = Thread()
        thread.add_message(Message(
            role="user",
            content=f"Research {topic} and summarize findings"
        ))
        
        research_tasks.append(agent.run(thread))
    
    # Run all research in parallel
    results = await asyncio.gather(*research_tasks)
    return results

2. Caching Agent Responses

Cache responses from specialized agents:

from functools import lru_cache

class CachedCoordinator:
    def __init__(self):
        self.agents = {}
        self.cache = {}
    
    async def delegate_with_cache(self, agent_name: str, task: str):
        cache_key = f"{agent_name}:{task}"
        
        if cache_key in self.cache:
            return self.cache[cache_key]
        
        result = await self.agents[agent_name].go_for_task(task)
        self.cache[cache_key] = result
        return result

3. Agent Pool Management

Reuse agent instances:

class AgentPool:
    def __init__(self, agent_config: Dict):
        self.available = {name: Agent(**config) 
                         for name, config in agent_config.items()}
        self.busy = set()
    
    async def get_agent(self, agent_type: str):
        if agent_type in self.available:
            agent = self.available.pop(agent_type)
            self.busy.add(agent)
            return agent
        else:
            # Wait for agent to become available
            await asyncio.sleep(0.1)
            return await self.get_agent(agent_type)
    
    def release_agent(self, agent: Agent):
        self.busy.remove(agent)
        self.available[agent.name] = agent

Error handling

Handle delegation failures gracefully:

class ResilientCoordinator:
    def __init__(self, agents: List[Agent], fallback_agent: Agent):
        self.agents = agents
        self.fallback = fallback_agent
    
    async def delegate_with_fallback(self, task: str):
        for agent in self.agents:
            try:
                thread = Thread()
                thread.add_message(Message(role="user", content=task))
                result = await agent.run(thread)
                return result
            except Exception as e:
                print(f"Agent {agent.name} failed: {e}")
                continue
        
        # All agents failed, use fallback
        thread = Thread()
        thread.add_message(Message(role="user", content=task))
        return await self.fallback.run(thread)

Testing multi-agent systems

from tyler.eval import AgentEval, Conversation, Turn, Expectation

# Test agent delegation
eval = AgentEval(
    name="delegation_test",
    conversations=[
        Conversation(
            id="multi_step_task",
            turns=[
                Turn(
                    role="user",
                    content="Research Python async patterns and write example code",
                    expect=Expectation(
                        uses_delegation=["Researcher", "Coder"],
                        completes_task=True
                    )
                )
            ]
        )
    ]
)

# Run evaluation
results = await eval.run(coordinator)

Next steps

Tool Integration

Add tools to your specialized agents

Testing Agents

Test your multi-agent systems

Performance

Optimize agent coordination

Examples

See agent delegation examples